@Article{cmc.2020.011608,
AUTHOR = {Phyu Hnin Thike, Zhaoyang Zhao, Peng Liu, Feihu Bao, Ying Jin, Peng Shi, *},
TITLE = {An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2091--2109},
URL = {http://www.techscience.com/cmc/v65n3/40157},
ISSN = {1546-2226},
ABSTRACT = {The optimization of network topologies to retain the generalization ability by
deciding when to stop overtraining an artificial neural network (ANN) is an existing vital
challenge in ANN prediction works. The larger the dataset the ANN is trained with, the
better generalization the prediction can give. In this paper, a large dataset of atmospheric
corrosion data of carbon steel compiled from several resources is used to train and test a
multilayer backpropagation ANN model as well as two conventional corrosion prediction
models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning is performed to develop multiple hyperparameter
combinations (network topologies) to train multiple ANNs with mini-batch stochastic
gradient descent optimization algorithm to facilitate the training of a large dataset. After
that, one selection strategy for the optimal hyperparameter combination is applied by an
early stopping method to guarantee the generalization ability of the optimal network
model. The correlation coefficients (R) of the ANN model can explain about 80% (more
than 75%) of the variance of atmospheric corrosion of carbon steel, and the root mean
square errors (RMSE) of three models show that the ANN model gives a better
performance than the other two models with acceptable generalization. The influence of
input parameters on the output is highlighted by using the fuzzy curve analysis method.
The result reveals that TOW, Cl- and SO2 are the most important atmospheric chemical
variables, which have a well-known nonlinear relationship with atmospheric corrosion.},
DOI = {10.32604/cmc.2020.011608}
}